@inproceedings{macavaney-etal-2018-rsdd,
title = "{RSDD}-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses",
author = "MacAvaney, Sean and
Desmet, Bart and
Cohan, Arman and
Soldaini, Luca and
Yates, Andrew and
Zirikly, Ayah and
Goharian, Nazli",
editor = "Loveys, Kate and
Niederhoffer, Kate and
Prud{'}hommeaux, Emily and
Resnik, Rebecca and
Resnik, Philip",
booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
month = jun,
year = "2018",
address = "New Orleans, LA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0618",
doi = "10.18653/v1/W18-0618",
pages = "168--173",
abstract = "Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one{'}s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.",
}
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<abstract>Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one’s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.</abstract>
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%0 Conference Proceedings
%T RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
%A MacAvaney, Sean
%A Desmet, Bart
%A Cohan, Arman
%A Soldaini, Luca
%A Yates, Andrew
%A Zirikly, Ayah
%A Goharian, Nazli
%Y Loveys, Kate
%Y Niederhoffer, Kate
%Y Prud’hommeaux, Emily
%Y Resnik, Rebecca
%Y Resnik, Philip
%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, LA
%F macavaney-etal-2018-rsdd
%X Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one’s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.
%R 10.18653/v1/W18-0618
%U https://aclanthology.org/W18-0618
%U https://doi.org/10.18653/v1/W18-0618
%P 168-173
Markdown (Informal)
[RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses](https://aclanthology.org/W18-0618) (MacAvaney et al., CLPsych 2018)
ACL
- Sean MacAvaney, Bart Desmet, Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly, and Nazli Goharian. 2018. RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 168–173, New Orleans, LA. Association for Computational Linguistics.